Today's contemporary energy environment is characterized by high energy prices, government mandates to enhance energy efficiency, and a strong drive to transition to renewable energy resources. Combined, these factors are putting strong pressure on building owners and managers to make lowering energy consumption and cutting energy costs a major priority. This pressure is further compounded by the ever increasing number of energy efficiency regulations and standards such as Congress' Energy Independence and Security Act of 2007 and the federal government's Energy Star rating program.
While a lot of emphasis has been placed on the use of high efficiency energy products and the purchase of clean and renewable energy resources, what is often missing from this strategy is waste elimination; waste that is embedded in the daily operations of most commercial, governmental, and industrial facilities; waste that is so prevalent and so widespread—but to which most Americans are oblivious. It begins at the doorstep of our office buildings and apartments; in our parking lots and garages; and it is prevalent inside our homes, malls, and offices—a waste which is conservatively estimated at 20% to 30% of our daily use of energy. Non-limiting examples are: Hallway and stairway lights which stay on at full capacity 100% of the time even when people are not present; parking lots in apartment and office buildings which stay lit like Christmas trees 100% of the time even when traffic is minimal in the wee hours of the morning; office lights, task lights, monitors, and computers which stay on 24 hours a day despite the fact that no one is there to use them after working hours; escalators and moving walkways which run continuously regardless of whether people are riding them or not; restroom lights at restaurants and retail outlets which stay on 24-hour a day, when long stretches of time go by without anyone using them. These are but a few of the most glaring examples of wasteful energy use practices currently prevalent in the United States. Most of this waste can be quickly eliminated with the installation of cheap motion sensors and simple “On/Off” switches.
In fact, eliminating operating waste is the quickest, most economical, and most environmentally friendly way to save energy and cut costs.
In certain examples, it may be desirable to provide waste reduction strategies at organizations, large and small, that provide tools to identify, quantify (measure), and monetize operating waste and savings opportunities; that can explain usual and unusual patterns in energy consumption; that can help develop and evaluate highly efficient and effective energy management solutions; and that can accurately measure and report performance results—on a continuous basis. In certain instances, without the availability of such tools, there may be less incentive to take action.
In certain instances, an impediment for the implementation of waste reduction strategies is lack of time, knowledge, and resources. Most large office buildings are owned by investment trusts or relatively small organizations that do not have an energy management team on staff; these buildings are typically run by building managers who are too busy tackling their day-to-day responsibilities of keeping their tenants happy to take the time or even to have the knowledge to use commercially available software to analyze, investigate, diagnose, and document the daily energy operations of their facilities. In fact, when it comes to energy, the most important issue for most building managers is energy budgeting; insuring that enough funds are available to pay their building's utility bills in a timely manner. In some instances, systems and/or methods providing more than simply energy budgeting may be desirable.
Moreover, much commercially available energy management software today is geared towards large enterprises that manage a large number of facilities. This kind of software, whether web-based or desktop based, is in some cases a “cookie-cutter/one-size-fits-all” approach to energy management; it organizes billing and energy consumption data into eye-catching charts and graphs. In certain examples, it may be desirable to also provide context, particularly taking into consideration the different types of facilities, such as whether the facility in question is a major office building, a warehouse, a hospital, a townhouse, or a major data processing center, and in some instances tailoring the analytical and/or reporting approach to at least the type of facility. While most enterprise software can flag the possibility of billing, operational, and mechanical problems, they typically leave it to the “energy manager” to analyze, investigate, diagnose, monetize, and document the possibility of such problems. Furthermore, despite the fact that several commercial packages also provide for the charting of energy interval pulse data from sophisticated metering devices—some, in real-time, in addition to identifying the possibility that an operating, mechanical, or metering problem has occurred, it may be desirable to quantify, monetize, or to explain the usual and unusual patterns that are identified.
A variety of systems and services currently on the market provide sophisticated energy tracking and monitoring of energy data. However, it may be desirable to provide quick identification, quantification, and monetization of operating waste and savings opportunities. In some instances, it may be desirable to explain the usual and unusual patterns in energy consumption identified by certain systems; as well as quickly identify effective and/or efficient energy management solutions, and it also may be desirable to provide accurate measurements of implemented solutions.
In order to better understand the major shortcomings of existing systems and methods in optimizing energy operations and eliminating operating waste at large facilities one can compare them to trying to help an overweight person reduce his weight; the best that existing systems and methods have to offer involves providing facility operations engineers with systems that can measure and plot energy consumption at their facilities in real-time—down to a 15-minute or 30-minute interval level—and not much else. Such systems are not known to include a knowledge base or an “Expert System” that can inform facility operators of their tenants' operations, the nature of the equipment they use and the list, power rating, and structure of their facility's mechanical and electronic systems. They also generally do not include any reference to the facility's applied utility rates structures nor provide any indication of the value—e.g., the cost of the energy—being saved at the facility.
Comparing that method to trying to help an overweight person lose weight is like providing that person with a device that can count his or her caloric intake and not much else! The age of that person, his or her physical health; profession (whether the person is a baggage handler or a switchboard operator) do not get taken into consideration. Progress is measured at the end of each month when the weight of the person is measured against his weight at the end of the prior month. There is no measurement of the value of losing weight such as lower cholesterol level, lower blood pressure, increased stamina, and lower food costs.
Understandably one cannot expect meaningful results from a mere measurement of real-time energy consumption without context just as one cannot expect a person to undergo a major weight loss from the mere counting of caloric intake.
Elements missing from the above approach, in certain instances, may include:
(1) Lack of a Frame of Reference: Just providing a measurement of anything is meaningless without a frame of reference. For example just stating that someone is 200 lbs. is meaningless in the context of weight loss. If the person's prior weight was 240 lbs. then that person has reduced his/her weight. On the other hand, if that person's weight was 180 lbs. then that person has gained weight. Therefore, the mere providing of real-time energy consumption without a comparison to a prior benchmark is of little use.
(2) Content without Context: Only stating that someone is 200 lbs. in itself is practically meaningless. It does even not tell if a person is heavy or slim. 200 lbs. on a 5′4″ frame would make a person very heavy. On the other hand, 200 lbs. on a large-framed 6′4″ person is likely to project an image of fitness and health, and a linebacker who weighed only 200 lbs. would be considered underweight. Therefore, merely providing real-time energy consumption without a context to what type of facility is being tracked (hospital, theater, or office building), the type of tenants it has, and the type of equipment it contains, is practically meaningless.
(3) Missing Valuation: The underlying principle of modern economic society is money. Money is representative of value. People work for money, they start businesses to make money, and they sacrifice to save money. Therefore, only providing a tool that helps reduce the use of a particular commodity (i.e. energy) without knowing the quantity that was reduced and being able to measure the value of the reduced quantity is practically meaningless.
Below is an analysis of certain offerings of major energy management companies in the United States. None of these companies provide a comprehensive system or method that is capable of quickly interpreting usual and unusual patterns in energy consumption; identifying, quantifying, and monetizing hidden operating and financial waste; and accurately measuring performance results.
For example, Energy Lens (www.energylens.com) created an Excel add-in that makes it easy to analyze detailed interval energy data. It allows users to study patterns in energy usage, and look for changes in energy performance. While useful for its intended purposes, Energy Lens' system merely flags the possibility that a problem may exist in the daily operations of a given facility. In certain instances, Energy Lens may be a basic analytical tool that identifies possible operating and mechanical errors. It may be desirable for a system to allow for the correlation of that data with the weather and/or provide for the synchronization of the operations data from one year to the next to quantify, and also measure differences in operations from one year to the next. It may also be desirable to provide an Expert system that could shed light into the nature of the facility's operations, or on the possible nature of any displayed irregular consumption data. And finally, it may be desirable to provide a cost modeling feature that could monetize the effect of the discovered irregular operations on operating costs.
LPB Energy Management (http://um-online.lpbenergy.com/umo_mansfield/Login.aspx) is an energy management company that is currently being acquired by Ecova. LPB provides a web-based program that generates reports and graphs displaying usage and cost statistics, and identifies potential billing errors and usage anomalies, and compares facilities in order to target those with the greatest savings potential. This system provides more capabilities than Energy Lens' system. This system would be a good match for managing multiple small facilities that operate in a similar fashion such as 7-Eleven, Macy's, and Best Buy stores, or organizations with a multitude of small facilities. However, it would be desirable, given the natural variation in energy bills, to provide operations benchmarking and year over year comparison, and to explain any variation in use, price, and cost; in order to help facility owners, and managers achieve greater energy efficiency. In summary, it would be desirable to provide utilities tracking software with the capability to identify, quantify, or monetize operating waste and savings opportunities.
Ecova (www.ecova.com) lists “Facility Optimization and Efficiency” as one of its solution areas. While Ecova's system does leverage the use of smart meters and building automation systems to generate real-time information streams that help identify “poor performers and outliers,” and automatically alert users to conditions outside of pre-defined ranges, its system's mainly identifies possible operating and mechanical errors in real-time mode. It may be desirable to incorporate an Expert system that provides information that helps with the accurate interpretation of the energy information, cost modeling capabilities, and capabilities for the quick identification, quantification, and monetization of operating waste and savings opportunities.
First Fuel (http://firstfuel.com) has a Rapid Building Assessment platform which uses building consumption data from the utility company in order to “see” into buildings and understand how energy is being used through end-use benchmarking and then provide actionable recommendations. However, in some instances, it may be desirable to provide for synchronized comparative visual analysis with a designated baseline in order to measure or quantify performance, and/or to allow users the ability to comment on the provided information interactively in order to share and exchange ideas on the nature of discovered problems and opportunities. It may also be desirable to take into account financial information that may enable useful “recommendations” to be made. It may also be desirable to provide methods or systems that include an “Expert” system to enable users to quickly interpret the visual information unless they are intimately knowledgeable with the facility's operations.
Tangible Software Inc. (www.tangiblesoftware.com) provides an energy software system that addresses six areas of an enterprise's energy infrastructure: invoice management; acquisition and contract management; building consumption and control; meter data management; demand management; and carbon footprint management. While Tangible Software Inc.'s system does provide real-time interval data capture, it may be desirable to provide systems or methods that provide the ability to generate synchronized comparative visual analyses of the information with a designated baseline, an Expert system for the interpretation of the information, and/or a cost modeling capability to monetize possible problems and opportunities.
iEnergyIQ (http://ienergyiq.com) is an Enterprise Energy Management company that provides software as a service and energy analytics for enterprises. These services include the ability to generate customized reports and graphs, as well as identify potential billing errors and usage anomalies. It is also capable of analyzing pulse data from meters and sub-meters. However, while iEnergyIQ is capable of comparing one meter's stream of pulse data with another account's steam of pulse data, it may be desirable to provide systems and/or methods providing synchronized comparative visual analyses of the information with a designated baseline, an Expert system for the interpretation of the information, and/or financial modeling capabilities.
eSightEnergy (www.esightenergy.com) is primarily an energy management system for enterprises. It offers a robust suite of web-based online modules with various levels of functionality as well as a desktop centric version. Basically, this system allows users to track and integrate energy consumption from meters located in disparate locations and generate consolidated reports. It is similar to the iEnergyIQ model described above. It may be desirable to provide systems and/or methods, however, that provide interpretive analyses, quantification and monetization of identified usage anomalies.
EPS (www.epsway.com) bills itself as a leader in energy management solutions for industrial manufacturers. Its approach includes performing an energy audit on the subject facility, making recommendation on replacing inefficient assets and shifting to alternate sources of energy. This company's energy efficiency solutions are based on using its xChange Point solution. This company's offering is more of an approach than a system—and it geared towards industrial facilities with limited application in the commercial office building market. The approach includes installing meters to monitor energy usage of systems and processes at an industrial facility, installing web-based software to enable access to the systems “near real-time” data, and have the company's team of energy experts make monthly recommendations on how to improve energy efficiency. However, it may be desirable to further include an “Expert” module and/or or a financial modeling part with systems such as this.
Other web-based energy and utility management systems are known such as EnergyCap (www.energycap.com), Abraxas Energy Consulting (www.abraxasenergy.com), Adapt Engineering (www.adaptengr.com), Amersco, (www.ameresco.com), Dynamic Energy Concepts (www.dynamicenergyconcepts.com), Energy-Accounting.com (www.energy-accounting.com), Enernoc (www.enernoc.com), Honeywell (www.honeywell.com), Pace Global (www.paceglobal.com), Pure Energy Management (www.pureenergymgmt.com), Utility Management Services (www.utilmanagement.com), EnergyWatchDog (www.energywatchdog.com).
All of the companies listed above can provide systems that can track and monitor billing and operations data and identify possible billing and operating anomalies at various levels. However, it may be desirable to provide systems and methods for an integrated solution that may enable users to quickly interpret and explain unusual patterns in energy consumption, that can quickly identify, quantify and monetize operating waste and savings opportunities; or that can quickly and accurately measure performance results—on a continuous basis, in certain example embodiments. Therefore, a need exists for methods and systems that are capable of quickly interpreting usual and unusual patterns in energy consumption; identifying, quantifying, and monetizing hidden operating and financial waste; and accurately measuring performance results—in a reduced amount of time, with reduced cost and effort.
To meet such challenges, for example, certain example embodiments herein provide an Expert system to identify, understand and explain usual and unusual operating profiles and/or the ability to measure and quantify energy consumption changes (increases/decreases) from the prior year by synchronizing back exactly (in preferred example implementations) 364 days (or multiples of that number, i.e., 728, 1092, 1456 for going back to a base year up to 4 years in arrears). Synchronizing with 363 or 365 days will result in Mondays being synchronized with Sundays and/or Fridays being synchronized with Saturdays. Also, synchronizing one month, three months, or six months in arrears will be less meaningful, as it could straddle seasons where operations are drastically different. This leave the 364 days (exactly 52 weeks) as an example meaningful synchronization method, in certain example embodiments. In other examples, 350, 357, 371, and/or 378 days may also be close enough so as to result in meaningful synchronization. Correlation can be displayed at the level of one day, one week, one month, or one year, and anything in between, according to different example embodiments.
Furthermore, by adding a financial “What If” module, one can monetize the quantity of energy that is varying from one year to the next.
Certain example systems and methods disclosed herein may be implemented in different ways. For example, in certain instances, a system may be applied/method implemented may include performing synchronization “retroactively” once the data has been made available by the utility company. As another example, in other instances, a “Real-Time” system may be applied/method may be implemented wherein the comparison were made between a prior baseline and the current data in real-time. This second approach may be as effective as the first approach in certain examples, and may trigger automatic action based on pre-determined criteria in some instances.
Furthermore, since both consumption and weather data will be collected concurrently by the system in certain example embodiments, the system may be able to perform regression analysis to determine the correlation and sensitivity of the changes in energy data to the changes in weather data. The change of correlation or sensitivity to the weather from one year to the next can be a useful indicator or a trigger for automated action in “Real-Time” systems.
Additional non-limiting example features and advantages include:                The synchronization of current or recent operating patterns with the corresponding operating patterns of the prior year—or any other pre-selected based year.        A synchronization that is based on a 364 days difference from one year to prior year as well as any multiple of that number (364) for each additional year in excess of one year.        The synchronization can start by any day of the week—not necessarily by Monday. For example, the weekly synchronization can start on a Sunday, or a Friday, or any other day.        A synchronization that can range from a comparison of hour to another, one day to another, up to one year and another—and anything in between.        A synchronization that allows for the identification of usual and unusual energy consumption patterns.        A synchronization than allows for the accurate measurement and quantification of the difference in energy consumption resulting from the difference in the displayed operating patterns.        The addition of an interactive commenting and discussion log that can reference, document, and explain the usual and unusual operating patterns displayed in the Operations Module.        The addition of tabular consumption, temperature, and cost data at the bottom the operating profiles of real-time data streams.        The combination of an “Expert” Module that would help in the interpretation and explanation of displayed operating patterns and leverages information collected from system participants to optimize the efficiency of commodity markets, collaborate on finding solutions to common problems, as well as discovering new technologies and the sharing of knowledge and ideas regarding the availability and the implementation of new energy systems, methods, and technologies.        The method of the composition of the “Expert” Module.        The combination of a “Costs” module that could monetize the difference in operating patterns from one year to the next as well as from one year to a preselected base year.        The use of the above methods separately or in combination with each other.        Triggering automated notifications and visual and audible warning signals whenever certain combinations of operating conditions and temperature differences (from the prior year or a preselected base year) have been met.        Triggering predetermined Action Control Scripts based whenever certain combinations of operating conditions and temperature differences (from the prior year or a preselected base year) have been met.        Tracking is not limited to energy and weather on the chart, it can include other parameters of interest to the user, such as hotel vacancy rates, number of meals served, etc.        
In certain example embodiments, a method of synchronizing current and/or recent operating pattern(s) with corresponding operating pattern(s) of a prior year may be provided. The method may comprise storing current and/or recent operating patterns on at least one storage device, the current and/or recent operating patterns comprising current and/or recent incremental commodity usage data. The method may further comprise storing operating patterns of at least a year prior to the current and/or recent operating patterns on the at least one storage device, the prior year operating patterns comprising historical incremental commodity usage data. The current and/or recent operating patterns may be synchronized, via a processor coupled to the storage device, with operating patterns of at least a year prior, based at least in part on periodically-repeating time periods over which the usage occurred to generate an incremental historical comparison of usage from different but related periodically-repeating time, that at least partly takes into account time-variable factors affecting the usage, including using the processor to automatically correlate usage data that is exactly an integer multiple of 364 days apart, in certain example embodiments.
Other example embodiments may relate to a system for monitoring and reporting usage of a commodity such as energy. The system may include at least one storage device configured to store historical incremental commodity usage data. The system may further include a processor coupled to the storage device, the processor time-correlating the historical incremental usage data based at least in part on periodically-repeating time periods over which the usage occurred to generate an incremental historical comparison of usage from different but related periodically-repeating time periods that at least partly takes into account time-variable factors affecting the usage. Furthermore, in certain instances, the processor may automatically identify unusual usage patterns by comparing with synchronized historic usage patterns and automatically providing contextual interactivity for the usage patterns based on a stored knowledge base, temperature- and/or consumption-based action control lists, and information including commodity pricing, and/or facilities analytics.
In further example embodiments, a system for tracking performance of a facility relating to energy and/or water usage may be provided. In certain instances, the system may include at least one storage device configured to store historical and/or current incremental commodity usage and/or cost data. The system may further comprise a first computer-implemented module comprising a plurality of components, the components comprising a facility information component, a cost analysis component, and an operations analysis component. In certain cases, the facility information component may comprise data pertaining to a particular facility, comprising a description of the facility, a function of the facility, operating hours of the facility, and/or information relating to usage of utilities by the facility, stored on the storage device. The cost analysis component comprising historical billing data may be stored on the storage device. The operations analysis component may comprise data corresponding to a plurality of weekly modules comprising 15 to 30 minute interval usage and weather data for each day of the week. Additionally, a second computer-implemented module may comprise periodic updates to the cost analysis and operations analysis components. The system may also include a processor configured to execute the first and second computer-implemented modules. Furthermore, the data corresponding to the weekly modules from a prior year and from a current year may be synchronized by day of week such that data from a Monday of a particular week in the prior year and data from a Monday of a corresponding week in the current year are synchronized in order to accurately track performance of the facility with respect to energy and/or water usage. In certain example embodiments, this synchronization may result in data from a given day being correlated with data from a day 364 days prior to the given day.
In other example embodiments, the modules may be daily rather than weekly. Further, rather than synchronizing by day of the week, the data corresponding to the modules from a prior year and from a current year are synchronized by day of week such that data from the given day and data from a day 364 days before the given day and/or a multiple thereof are synchronized in order to accurately track performance of the facility with respect to energy and/or water usage.
Further example embodiments relate to a method of tracking performance trends relating to utility usage. The method may comprise synchronizing 15 to 30 minute intervals of data from a particular week in first year with 15 to 30 minute intervals of data from a corresponding week in a second year such that the data is synchronized by day of the week in order to track the performance of utility usage from the particular week of the first year to the corresponding week of the second year by comparing usage from the week of the first year and the week of the second year in 15 to 30 minute intervals.
Still further example embodiments relate to a non-transitory storage medium arrangement for in use being operatively coupled to a computing device. The computing device in use may access the storage medium arrangement to generate an output including at least user display data. The storage medium arrangement may store at least instructions executable by computing device to correlate data stored on the storage medium arrangement and generate said user display data output. In certain examples, the storage medium arrangement may have stored thereon: utility usage data defining at least utility usage including an amount of usage and time periods corresponding to said usage; and instructions executable by said computing device to process usage amount at least in part based on said time periods to correlate and register said usage data accordingly to utility demand patterns that are likely to recur in time, to thereby enable comparison between time-comparable demand cycle patterns.
In certain example embodiments, the correlation may be performed by aligning a given day in a current year with the day that is exactly 364 days before the given day. In other example embodiments, the correlation and/or synchronization may be with a day that is a multiple of 364 days prior to the given day, and/or with a day that is close to 364 days prior to the given day and is divisible by 7 (e.g., 350, 357, 371, 378, and the like).